Nearly all data shown here is from the South Africa National Institue for Communicable Diseases (NICD), but it is accessed through different channels. Cases, deaths, and testing data are retrieved from Our World in Data on GitHub via Johns Hopkins and NICD. Hospitalization data and provincial data are retrieved from from the Data Science for Social Impact Research Group @ University of Pretoria, Coronavirus COVID-19 (2019-nCoV) Data Repository for South Africa. Available on: https://github.com/dsfsi/covid19za. Many thanks to all who have worked to collect this data and make it publicly accessible.
I display data since the beginning of 2021. Dashed lines indicate the date (Nov 25, 2021) when the Omicron variant was announced by NICD. My processing and analysis code can be found here.
The line chart below shows the weekly growth multiplier of seven-day average cases. Values over 1 indicate case growth, while values under 1 mean case decline. For example, a 2.0 growth multiplier would mean cases are twice as high as the week before (rising); 0.5 would mean that they are only half as high (falling). Dots show daily values compared to seven days earlier.
Percentage of peak values This charts display the 7-day average for deaths (black) and cases (orange) over time, expressed as the percentage of the all-time high values reached in summer 2021. Deaths are lagged by 17 days, the observed gap between the peak of cases and the peak of deaths for South Africa as a whole during the summer of 2021 (Delta wave). It is designed to explore differences in disease severity over time.
Case fatality rate This chart displays the 7-day average for deaths (lagged 17 days) divided by the 7-day average for cases. The lag reflects the observed gap between the peak of cases and the peak of deaths during the summer of 2021 (Delta wave). The chart includes a loess smoothing.
Positive rate reflects the 7-day average for new reported cases divided by the 7-day average for new reported tests. When data from JHU/Our World in Data lags reported data, I instead use figures from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub that include data on cumulative tests from NICD press releases. Provincial weekly positive rates are also from NSFSI.
The chart below displays weekly positivity rates for South Africa and Gauteng province reported by NICD and catalogued by DSFSI. Past weeks may be updated as more test results are reported.
Data from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub. Presented first for South Africa as a whole and then for Gauteng Province specifically. DSFSI catalogs hospitalization data reported by NICD’s daily DATCOV hospital surveillance reports.
Percentage of Peak Values Case and hospitalization metrics (seven-day averages) over time as percentage of peak values for South Africa. No lags are applied. The gray area chart shows the progression of cases over time, while the lines show hospitalization metrics.
| var | date | total | weekday | new | avg_7day |
|---|---|---|---|---|---|
| cases | 2022-01-01 | 3468079 | Saturday | 9793 | 8591.71429 |
| cases | 2022-01-02 | 3472436 | Sunday | 4357 | 8413.71429 |
| cases | 2022-01-03 | 3475512 | Monday | 3076 | 8313.42857 |
| cases | 2022-01-04 | 3483590 | Tuesday | 8078 | 8436.57143 |
| cases | 2022-01-05 | 3494696 | Wednesday | 11106 | 8734.57143 |
| cases | 2022-01-06 | 3504554 | Thursday | 9858 | 8288.85714 |
| cases | 2022-01-07 | 3513813 | Friday | 9259 | 7932.42857 |
| cases | 2022-01-08 | 3521572 | Saturday | 7759 | 7641.85714 |
| deaths | 2022-01-01 | 91198 | Saturday | 53 | 60.71429 |
| deaths | 2022-01-02 | 91228 | Sunday | 30 | 59.14286 |
| deaths | 2022-01-03 | 91312 | Monday | 84 | 69.00000 |
| deaths | 2022-01-04 | 91451 | Tuesday | 139 | 85.28571 |
| deaths | 2022-01-05 | 91561 | Wednesday | 110 | 89.42857 |
| deaths | 2022-01-06 | 92112 | Thursday | 551 | 150.14286 |
| deaths | 2022-01-07 | 92252 | Friday | 140 | 158.14286 |
| deaths | 2022-01-08 | 92371 | Saturday | 119 | 167.57143 |